Vieira, J. C.Sartori, AndrezaStefenon, Stéfano FrizzoPerez, Fabio LuisSchneider De Jesus, GabrielLEITHARDT, VALDERI2023-02-012023-02-012022http://hdl.handle.net/10400.26/43566Due to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.engNeural networks,artificial neural networks,image processing,image classificationjournal article2022-03-12cv-prod-295038410.1109/ACCESS.2022.3155123